We propose a Bayesian framework for joint distributional forecasting of monthly asylum applications across the EU-27. The model decomposes latent application intensities into country-specific random walks and common factors, with idiosyncratic and shared shocks allowed to exhibit heavy tails or stochastic volatility. Using Eurostat data from 2008 to 2026, we evaluate predictive distributions in a rolling out-of-sample exercise, scoring overall distributional accuracy and upper-tail risk. Three findings emerge. First, the preferred specification varies across countries, scoring rules, and horizons, underscoring the need to align models with policy-specific loss functions. Second, joint EU-27 models improve on country-by-country benchmarks, with the largest gains in the upper tail, where preparedness costs are most relevant. Third, random-walk log-intensities provide a useful short-run description of national asylum-application dynamics, especially when combined with flexible innovation dynamics. We conclude by discussing implications for national and EU-level agencies involved in asylum forecasting and preparedness planning.

翻译：我们提出一个贝叶斯框架，用于联合分布预测EU-27各国月度庇护申请数量。该模型将潜在申请强度分解为国家特定随机游走与共同因子，允许异质性与共享冲击呈现厚尾或随机波动特征。基于2008至2026年欧盟统计局数据，我们通过滚动样本外检验评估预测分布，重点考察整体分布准确性与上尾风险。研究得出三项发现：第一，最优模型设定因国家、评分规则及预测期限而异，凸显将模型与政策特定损失函数对齐的必要性；第二，EU-27联合模型较逐国基准模型表现更优，尤其在涵盖准备成本的上尾区域改进最为显著；第三，随机游走对数强度能有效描述国家庇护申请动态的短期特征，尤其在与灵活创新动态结合时表现更佳。最后，我们探讨了本研究对参与庇护预测与应急预案制定的国家及欧盟层面机构的启示。